Abstract
With exponentially increasing users and crucial information accumulation over the internet, it has become a necessity to introduce a system which is powerful in terms of providing protection, and effective in cost required in authentication process. Biometrics is the only thing that cannot be stolen or copied as every human being has their own unique features that cannot be imitated by any intruder. The only disadvantage of biometrics authentication process, the need of additional devices is removed in the proposed system. This method can make the computer uniquely identify a user by typing behavior and defeat intruders. As accurate as any other biometric security technique, keystroke biometrics is cost effective because it does not require any additional hardware. In this paper a new system is introduced having keystrokes biometric added with password hardening techniques as an effective authentication method to defeat intrusion attempts.
References
Keystroke Dynamics - Benchmark Data Set. http://www.cs.cmu.edu/~keystroke/
Karnan, M., Akilab, M., Krishnarajc, N.: Biometric personal authentication using keystroke dynamics: a review. Appl. Soft. Comput. 11, 1565–1573 (2011)
Gaines, R.S., Lisowski, W., Press, S.J., et al.: Authentication by keystroke timing: Some preliminary results. Rand Corp, Santa Monica CA (1980)
Young, J.R., Hammon, R.W.: Method and apparatus for verifying an individual’s identity. U. S. Patent No. 4, 805, 222. U.S. Patent and Trademark Office, Washington, DC (1989)
Monrose, F., Rubin, A.D.: Keystroke dynamics as a biometric for authentication, future gener. Comput. Syst. 16, 351–359 (2000)
Peacock, A., Ke, X., Wilkerson, M.: Typing pattern: a key to user identification. MIT. IEEE Secur. Priv. 2, 40–47 (2004)
Hu, J., Gingrich, D., Sentosa, A.: A k-nearest neighbor approach for user authentication through biometric keystroke dynamics. In: IEEE Conference on Communications, pp. 1556–1560 (2008)
Giroux, S., Wachowiak-Smolikova, R., Wachowiak, M.P.: Keystroke-based authentication by key press intervals as a complementary behavioral biometric. In: IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, TX, USA (2009)
Germain, R.S., Califano, A., Colville, S.: Fingerprint matching using transformation parameter clustering. IEEE Comput. Sci. Eng. 4, 42–49 (1997)
Wang, Y., Hu, J., Philip, D.: A fingerprint orientation model based on 2D Fourier Expansion (FOMFE) and its application to singular-point detection and fingerprint Indexing. Special Issue on Biometrics: Progress and Directions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 573–585 (2007)
Hosseinzadeh, D., Krishnan, S.: Gaussian mixture modeling of keystroke patterns for biometric applications. IEEE Trans. Syst. Man Cybernetics—Part C: Appl. Rev. 38 (2008)
Spillane, R.: Keyboard apparatus for personal identification. IBM Tech. Discl. Bull. 17, 3346 (1975)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Abhimanyu, Rathee, T. (2016). Keystroke Dynamics: Authenticating Users by Typing Pattern. In: Unal, A., Nayak, M., Mishra, D.K., Singh, D., Joshi, A. (eds) Smart Trends in Information Technology and Computer Communications. SmartCom 2016. Communications in Computer and Information Science, vol 628. Springer, Singapore. https://doi.org/10.1007/978-981-10-3433-6_46
Download citation
DOI: https://doi.org/10.1007/978-981-10-3433-6_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3432-9
Online ISBN: 978-981-10-3433-6
eBook Packages: Computer ScienceComputer Science (R0)